nn_max_pool2d function

MaxPool2D module

Applies a 2D max pooling over an input signal composed of several input planes.

nn_max_pool2d(
  kernel_size,
  stride = NULL,
  padding = 0,
  dilation = 1,
  return_indices = FALSE,
  ceil_mode = FALSE
)

Arguments

• kernel_size: the size of the window to take a max over
• stride: the stride of the window. Default value is kernel_size
• padding: implicit zero padding to be added on both sides
• dilation: a parameter that controls the stride of elements in the window
• return_indices: if TRUE, will return the max indices along with the outputs. Useful for nn_max_unpool2d() later.
• ceil_mode: when TRUE, will use ceil instead of floor to compute the output shape

Details

In the simplest case, the output value of the layer with input size $(N, C, H, W)$, output $(N, C, H_{out}, W_{out})$ and kernel_size $(kH, kW)$

can be precisely described as:

If padding is non-zero, then the input is implicitly zero-padded on both sides for padding number of points. dilation controls the spacing between the kernel points. It is harder to describe, but this link has a nice visualization of what dilation does.

The parameters kernel_size, stride, padding, dilation can either be:

• a single int -- in which case the same value is used for the height and width dimension
• a tuple of two ints -- in which case, the first int is used for the height dimension, and the second int for the width dimension

Shape

• Input: $(N, C, H_{in}, W_{in})$
• Output: $(N, C, H_{out}, W_{out})$, where

Examples

if (torch_is_installed()) {
# pool of square window of size=3, stride=2
m <- nn_max_pool2d(3, stride = 2)
# pool of non-square window
m <- nn_max_pool2d(c(3, 2), stride = c(2, 1))
input <- torch_randn(20, 16, 50, 32)
output <- m(input)
}